Adversarial Robustness
Adversarial Robustness
Testing and improving AI systems' resilience to adversarial inputs and attacks
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Testing and improving AI systems' resilience to adversarial inputs and attacks
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AI safety research nonprofit founded in 2022 by Adam Gleave and Karl Berzins, focusing on making AI systems safe through technical research and coo...
When AI systems fail due to differences between training and deployment contexts. Research shows 40-45% accuracy drops when models encounter novel ...
Mathematical framework analyzing how layered AI safety measures combine, showing independent layers with 20-60% failure rates can achieve 1-3% comb...
Technical AI safety research aims to make AI systems reliably safe through scientific and engineering work. Current approaches include mechanistic ...
This model analyzes how alignment robustness changes with capability scaling. It estimates current techniques maintain 60-80% robustness at GPT-4 l...